{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An exploratory data analysis (EDA) shows the general demographic distribution of the ECG database. Age and gender of participants can highlight potential bias of the data to take into account when using the trained model. Correlations and associations between selected features are also examine to identify possible redundant information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing packages\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import scipy.signal\n",
    "import glob\n",
    "import os\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 DataFrame analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature distributions: Is there a bias in gender or/and age?\n",
    "Even if there is a slighlty higher amount of males in the sample population, the gender distributions is not indicate of a strong bias. Instead, younger subjects (around the age of 20) composed most of the sample population, thus making an indication on how this can impact the performance of the model in a different population. Moreover, if we take into account the possibility of the impact of aging in the ECG waveform, then is not a minor issue to consider. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ecg = pd.read_pickle(\"df_ecg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender distribution\n",
    "plt.style.use(\"seaborn\")\n",
    "\n",
    "gender_vals = pd.Series(data=df_ecg[\"Gender\"].value_counts())\n",
    "((gender_vals/df_ecg.shape[0])*100).plot(kind='bar')\n",
    "plt.title(\"Gender distribution of subjects\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"% of subjects\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age distribution\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "ax.hist(df_ecg[\"Age\"]);\n",
    "ax.set(title=\"Age distribution of subjects\",\n",
    "      xlabel=\"Age (years)\",\n",
    "      ylabel=\"Number of subjects\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age Category-Gender analysis\n",
    "df_ecg['age_category'] = df_ecg['Age'].apply(lambda x: 'Child' if x <= 18 else ('Young Adult' if x <= 40 else ('Adult' if x <= 65 else 'Senior')))\n",
    "df_ecg = df_ecg.join(pd.get_dummies(df_ecg['Gender']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ver = df_ecg.groupby(by=['age_category']).sum()\n",
    "female = df_ver.female.values\n",
    "male = df_ver.male.values\n",
    "index = ['Child', 'Young Adult', 'Adult', 'Senior']\n",
    "df_bar = pd.DataFrame({'female': female,\n",
    "                       'male': male}, index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_bar.plot.bar(rot=0)\n",
    "ax.set(title=\"\",\n",
    "      xlabel=\"Age Category\",\n",
    "      ylabel=\"Number of subjects\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# RR interval distribution\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "ax.hist(df_ecg[\"RR\"]);\n",
    "ax.set(title=\"RR interval distribution of patients\",\n",
    "      xlabel=\"RR interval (s)\",\n",
    "      ylabel=\"Number of subjects\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ecg.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following queries are not very enlightening since it seems there is not a clear association between demographic and calculated ECG features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "female    26.281996\n",
       "male      31.271003\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ecg.groupby([\"Gender\"]).mean()[\"Age\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "27    1.414231\n",
       "51    1.419217\n",
       "58    1.423704\n",
       "49    1.547803\n",
       "19    1.552456\n",
       "22    1.621304\n",
       "28    1.639825\n",
       "45    1.670613\n",
       "75    1.680522\n",
       "32    1.730326\n",
       "23    1.742360\n",
       "59    1.744381\n",
       "36    1.749311\n",
       "25    1.760200\n",
       "20    1.769429\n",
       "34    1.774381\n",
       "44    1.790245\n",
       "21    1.799863\n",
       "13    1.809524\n",
       "39    1.816418\n",
       "40    1.827805\n",
       "17    1.830235\n",
       "46    1.831245\n",
       "48    1.895617\n",
       "16    1.899987\n",
       "68    1.984214\n",
       "55    2.012162\n",
       "24    2.024041\n",
       "31    2.053246\n",
       "Name: RR, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ecg.groupby([\"Age\"]).mean()[\"RR\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "male      1.723649\n",
       "female    1.822028\n",
       "Name: RR, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ecg.groupby([\"Gender\"]).mean()[\"RR\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "75   -0.000671\n",
       "59   -0.000134\n",
       "19    0.000903\n",
       "28    0.001183\n",
       "31    0.001747\n",
       "24    0.001858\n",
       "39    0.001970\n",
       "58    0.002449\n",
       "34    0.002528\n",
       "16    0.002592\n",
       "13    0.002677\n",
       "27    0.002699\n",
       "49    0.002725\n",
       "68    0.002843\n",
       "46    0.003327\n",
       "55    0.003513\n",
       "48    0.003520\n",
       "22    0.003594\n",
       "44    0.003745\n",
       "25    0.003750\n",
       "23    0.004390\n",
       "32    0.004542\n",
       "21    0.004758\n",
       "36    0.005291\n",
       "17    0.005588\n",
       "40    0.005726\n",
       "45    0.005817\n",
       "20    0.007466\n",
       "51    0.010850\n",
       "Name: ECG_mean, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ecg.groupby([\"Age\"]).mean()[\"ECG_mean\"].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "male      0.003383\n",
       "female    0.004386\n",
       "Name: ECG_mean, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ecg.groupby([\"Gender\"]).mean()[\"ECG_mean\"].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heatmap shows only a strong (and clearly expected) correlation between standar deviation and variance, and a less strong one between median and mean, variance and standar deviation. This could indicate that there is redundancy among the calculated features, and thus, the model could suffer for the lack of additional trends, patterns or general information to perform classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_ecg.corr(), annot=True, fmt=\".2f\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample population shows a more or less balance gender distribution but a bias towards younger subjects, which could impact the model's performance in a different database.\n",
    "It seems that calculated features could contain redundant information, presenting an additional difficulty for the model to recognize a broader set of subjects. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
